LSTM模型中的时间步长到底是什么? [英] What exactly is timestep in an LSTM Model?

问题描述

我是LSTM和RNN的新手，我一直在绞尽脑汁了解确切的时间步伐.我真的很希望对此做出直观的解释

解决方案

让我们以克里斯·奥拉(Chris Olah)博客的出色形象开始(高度

在递归神经网络中，同一细胞有多个重复.推理的方式是-接受一些输入(x ₀ )，将其通过单元格以获取一些输出 ₁ (在图片的右侧用黑色箭头表示))，然后将output ₁ 作为输入(可能会在图像上添加一些其他输入分量-x ₁ )到同一单元格，产生新的输出output ₂ ，再次将其作为输入传递到同一单元格(同样可能带有附加的输入分量x ₂ )，产生输出 ₃ ，依此类推.

时间步是单元格的一次出现-例如在第一个步骤中，您生成输出 ₁ ，h ₀ ，在第二个步骤中，生成输出 ₂ ，依此类推.

I am a newbie to LSTM and RNN as a whole, I've been racking my brain to understand what exactly is a timestep. I would really appreciate an intuitive explanation to this

解决方案

Let's start with a great image from Chris Olah's blog (a highly recommended read btw):

In a recurrent neural network you have multiple repetitions of the same cell. The way inference goes is - you take some input (x₀), pass it through the cell to get some output₁(depicted with black arrow to the right on the picture), then pass output₁ as input(possibly adding some more input components - x₁ on the image) to the same cell, producing new output output₂, pass that again as input to the same cell(again with possibly additional input component x₂), producing output₃ and so on.

A time step is a single occurrence of the cell - e.g. on the first time step you produce output₁, h₀, on the second time step you produce output₂ and so on.

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